1. Field of the Invention
The present invention relates to the petroleum industry, and particularly to the characterization of petroleum reservoirs by constructing a representation of the reservoir referred to as a “reservoir model.”
2. Description of the Prior Art
Optimization and development of petroleum reservoirs is based on the most accurate possible description of the structure, of the petrophysical properties, of the fluid properties, etc., of the reservoir. A tool which accounts for these two aspects is used in an approximate way. It is a model of the subsoil, representative both of its structure and of its behavior. Generally, this type of model is represented in a computer, it is then referred to as a numerical model.
These models, which are well known and widely used in the petroleum industry, allow determination of many technical parameters relative to prospecting, study or development of a reservoir such as a hydrocarbon reservoir for example. In fact, a reservoir model is representative of the structure of the reservoir and of the behavior thereof. It is thus, for example, possible to determine which zones are the likeliest to contain hydrocarbons, the zones in which it can be interesting/necessary to drill an injection well in order to enhance hydrocarbon recovery, the type of tools to use, the properties of the fluids used and recovered, etc. These interpretations of reservoir models in terms of “technical development parameters” are well known, even though new methods are regularly developed. It is thus crucial, in the petroleum industry, to construct a reservoir model as precisely as possible. Integration of all the available data is therefore essential.
The purpose of a reservoir model thus is to best account for all the information relative to a reservoir. A reservoir model is representative when a reservoir simulation provides historical data estimations that are very close to the observed data. What is referred to as historical data are the production data obtained from measurements in wells in response to the reservoir production (oil production, water production of one or more wells, gas/oil ratio (GOR), production water proportion (water cut)), and/or repetitive seismic data (4D seismic impedances in one or more regions, etc.). A reservoir simulation is a technique allowing simulation of fluid flows within a reservoir by software referred to as “flow simulator.”
History matching modifies the parameters of a reservoir model, such as permeabilities, porosities or well skins (representing damages around the well), fault connections, etc., in order to minimize the differences between the simulated and measured historical data. The parameters can be linked with geographic regions, such as permeabilities or porosities around one or more wells.
Assisted history matching techniques are widely used to characterize a reservoir by integrating well data and seismic data. The techniques described in the following documents are for example known:    Roggero, F. and Hu, L. Y.: “Gradual Deformation of Continuous Geostatistical Models for History Matching”, Paper SPE 49004, Proc. SPE Annual Technical Conference and Exhibition, New Orleans, USA, 1998;    Gosselin, O., Cominelli, A. van den Berg, S. and Chowdhury, S. D.: “A Gradient-Based Approach for History Matching of Both Production and 4D Seismic Data”, Proceeding 7th European Conference on the Math. of Oil Recovery, Baveno, Italy, 2000;    Cheng, H., Wen, X., Milliken, W. J. and Datta-Gupta, A.: “Field Experiences with Assisted and Automated History Matching”, Paper SPE 89857, SPE ATC&E, Houston, Tex., USA, 2004;    Roggero, F., Ding, D. Y., Berthet, P., Lerat, O., Cap, J. and Schreiber, P. E.: “Matching of Production History and 4D Seismic Data—Application to the Girassol Field, Offshore Angola”, Paper SPE 109929, SPE ATC&E, Anaheim, Calif., USA, 2007.
During history matching, an objective function that measures the differences between the observed historical data and the simulated historical data is first defined. The larger the number of parameters of the reservoir model, the more difficult the matching procedure is because more simulations are required to evaluate the objective function in order to find a better result.
Various optimization techniques have been developed to minimize the objective function. Approaches based on the gradient method (Gosselin, O., Cominelli, A. van den Berg, S. and Chowdhury, S. D.: “A Gradient-Based Approach for History Matching of Both Production and 4D Seismic Data”, Proceeding 7th European Conference on the Math. of Oil Recovery, Baveno, Italy, 2000) are widely used in history matching. Other approaches such as stochastic optimizations (Mohamed, L., Christie, M. and Demyanov, V.: “Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification”, Paper SPE119139 presented at SPE Reservoir Simulation Symposium, Houston, Feb. 2-4, 2009) or adaptive training algorithms (Feraille, M. and Roggero, F.: “Uncertainty Quantification for Mature Field Combining the Bayesian Inversion Formalism and Experimental Design Approach”, 9th European Conf. on Math. of Oil Recovery, Cannes, France, 30 Aug.-2 Sep. 2004) are sometimes also used. In all these approaches, the optimization methods are directly applied to the objective function containing all of the reservoir data.
Assisted history matching techniques are thus developed to help reservoir engineers improve the matching efficiency. This history matching is however generally a long and fastidious process which also requires great effort and expertise from reservoir engineers. These methods require many reservoir simulations in order to evaluate the objective function since the number of parameters is large. These techniques are therefore very costly in CPU time and they are not always directly suited to the needs of specialists in charge of the petroleum reservoir development. It is thus important to reduce the number of simulations in the optimization process.
In order to reduce the number of simulations, Maschio, C. and Schiozer, D. J.: “A New Methodology for Assisted History Matching Using Independent Objective Functions” Petroleum Science and Technology, v 26, n 9, p 1047-1062, June 2008, propose using independent objective functions. In this approach, the global objective function is decomposed into several totally independent objective functions and it is assumed that a parameter that influences an independent objective function must have no impact on other independent objective functions. In practice, it is very difficult to find cases verifying this hypothesis. Furthermore, this method minimizes the independent objective functions, without considering minimization of the global objective function.